Model estimation for solar generation forecasting using cloud cover data

Pepe, Daniele; Bianchini, Gianni; Vicino, Antonio

doi:10.1016/j.solener.2017.08.086

Cited by 13 publications

(11 citation statements)

References 28 publications

(41 reference statements)

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“…Normalizing (24) with respect to P cs max yields I cs (j) · α (I cs (j), T (j)) I cs max · α(I cs max , T (j max ))…”

Section: Clear-sky Data Detectionmentioning

confidence: 99%

“…• As previously stated, a good guess for the main power/irradiance gain µ 1 is represented bŷ µ 1 (0) = P nom /1000, where P nom denotes the nominal plant power [24,27]. As pointed out in Remark 1, it is appropriate to start with an underestimate of this value, e.g., 75%, to ensure faster parameter adaptation.…”

Section: Model Estimationmentioning

confidence: 99%

“…• As for the initial valuesμ 2 (0) andμ 3 (0), it is convenient to choose them so that µ 2 (0)/µ 1 (0) and µ 3 (0)/µ 1 (0) are equal to the central values of the intervals S 2 and S 3 in (5), respectively [24].…”

Section: Model Estimationmentioning

confidence: 99%

“…The time series representing the DA and HA forecasts can be constructed from the pointwise forecast (41) by letting j and q vary in suitable sets. The details are omitted here for the sake of brevity and the reader is referred to Section 5 of [24].…”

mentioning

confidence: 99%

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Estimation of photovoltaic generation forecasting models using limited information

2020

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This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.

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“…Normalizing (24) with respect to P cs max yields I cs (j) · α (I cs (j), T (j)) I cs max · α(I cs max , T (j max ))…”

Section: Clear-sky Data Detectionmentioning

confidence: 99%

Section: Model Estimationmentioning

confidence: 99%

Section: Model Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimation of photovoltaic generation forecasting models using limited information

2020

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“…However, the substantial impact of uncertainty of the solar irradiance forecast (especially, direct normal irradiance) on the solar power plants output and their profitability over time should be addressed. Moreover, much attention should been paid to the significance of acquiring hour-ahead or day-ahead forecasts of solar irradiance [2]. Accordingly, most recent studies have emphasized on attaining the best forecast accuracy based on high-quality solar irradiance data to reduce the effect of the intermittency nature of solar energy on the uncertainty in the optimal design parameters and the errors in all modeling and measurements [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

Al-Aboosi

2019

Int J Energy Environ Eng

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The precise estimation of solar radiation data is substantial in the long-term evaluation for the techno-economic performance of solar energy conversion systems (e.g., concentrated solar thermal collectors and photovoltaic plants) for each site around the world, particularly, direct normal irradiance which is utilized commonly in designing solar concentrated collectors. However, the lack of direct normal irradiance data comparing to global and diffuse horizontal irradiance data and the high cost of measurement equipment represent significant challenges for exploiting and managing solar energy. Consequently, this study was performed to develop two hierarchical methodologies by using various models, empirical correlations and regression equations to estimate hourly solar irradiance data for various worldwide locations (using new correlation coefficients) and different sky conditions (using cloud cover range). Additionally, the preliminary assessment for the potential of solar energy in the selected region was carried out by developing a comprehensive analysis for the solar irradiance data and the clearness index to make a proper decision for the capability of utilizing solar energy technologies. A case study for the San Antonio region in Texas was selected to demonstrate the accuracy of the proposed methodologies for estimating hourly direct normal irradiance and monthly average hourly direct normal irradiance data at this region. The estimated data show a good accuracy comparing with measured solar data by using locally adjusted coefficients and different statistical indicators. Furthermore, the obtained results show that the selected region is unequivocally amenable to harnessing solar energy as the prime source of energy by utilizing concentrating and non-concentrating solar energy systems.

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An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings

et al. 2019

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This paper deals with the problem of cost-optimal operation of smart buildings that integrate a centralized HVAC system, photovoltaic generation and both thermal and electrical storage devices.Building participation in a Demand-Response program is also considered. The proposed solution is based on a specialized Model Predictive Control strategy to optimally manage the HVAC system and the storage devices under thermal comfort and technological constraints. The related optimization problems turn out to be computationally appealing, even for large-scale problem instances. Performance evaluation, also in the presence of uncertainties and disturbances, is carried out using a realistic simulation framework.

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Model estimation for solar generation forecasting using cloud cover data

Cited by 13 publications

References 28 publications

Estimation of photovoltaic generation forecasting models using limited information

Estimation of photovoltaic generation forecasting models using limited information

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings

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